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:2019,28(3):255-259
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改进主题模型的短文本评论情感分析
(浙江理工大学 信息学院, 杭州 245000)
Short Text Comment Sentiment Analysis of Improved Topic Models
(School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China)
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投稿时间:2018-09-28    修订日期:2018-10-23
中文摘要: 使用传统的主题模型方法对医疗服务平台中的评论等短文本语料进行主题模型的情感分析时,会出现上下文依赖性差的问题。提出基于词嵌入的WLDA算法,使用Skip-Gram模型训练出的词w*替换传统的LDA模型中吉布斯采样算法里的词w`,同时引入参数λ,控制吉布斯采样时词的重采样的概率.实验结果证明,与同类的主题模型相比,该主题模型的主题一致性高.
中文关键词: 情感分类  短文本  词嵌入  WLDA
Abstract:When the traditional topic model method is used to analyze the sentiment of the topic model for short text corpora such as comments in the medical service platform, the problem of poor context dependency may occur. A WLDA algorithm based on word embedding is proposed. The word w* trained in the Skip-Gram model replaces the word w` in the Gibbs sampling algorithm in the traditional LDA model, and the parameter λ is introduced to control the resampling probability of the words during Gibbs sampling. The experimental results show that the subject model has a high degree of consistency compared to similar topic models.
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花树雯,张云华.改进主题模型的短文本评论情感分析.计算机系统应用,2019,28(3):255-259
HUA Shu-Wen,ZHANG Yun-Hua.Short Text Comment Sentiment Analysis of Improved Topic Models.COMPUTER SYSTEMS APPLICATIONS,2019,28(3):255-259

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